Image matching device and patient positioning device using the same

ABSTRACT

An image matching device in which, in automatic positioning calculation using an ICP method, a solution is converged to an optimal solution while avoiding reaching a local solution, and a patient positioning device using this matching device. The device includes a CT image data reading unit reading first and second CT image data, a point group data generation unit generating first and second point group data in a three-dimensional space by binarization processing and edge-extraction processing on sliced images of the first and second CT image data, respectively, a point group data resolution conversion unit thinning-out point group data so as to extend a point-group-data array pitch, and an ICP calculation unit obtaining, using an ICP method, a conversion amount for the second point group data so that an error function of the first point group data and the second point group data outputted from the point group data resolution conversion unit is minimized.

TECHNICAL FIELD

The present invention relates to an image matching device utilizing CT(computed tomography) image data, etc., and to a patient positioningdevice, using the image matching device, for radiation treatment, etc.

BACKGROUND ART

As a conventional image matching method using three-dimensional pointgroup data, an ICP (iterative closest point) method is present. The ICPmethod is a technique for estimating, in two sets of three-dimensionalpoint group data (G1, G2) to be image matching targets, by sequentialand iterative calculation, using the sum of distance values betweencorresponding points as an error function, based on least-squaresestimation, suitable position/attitude conversion parameters between thethree-dimensional point group data (translation amount and rotationamount), so as to minimize the error function.

In this ICP method, because all combinations of the corresponding pointsare calculated, explicit corresponding information between the matchingdata is not needed. That is, information that a point in thethree-dimensional point group data G1 and a point in thethree-dimensional point group data G2 are identical is not needed.Therefore, images can be matched without teaching anatomicalcharacteristic points, etc., by a human. Image matching methods usingthe ICP method are applied to various fields (for example, PatentDocument 1); in radiation treatment, by using as referencethree-dimensional CT image data for a treatment plan obtained when theradiation treatment is planned, an image according to this data and thatto three-dimensional CT image data for a positioning operation, withrespect to a bone structure, obtained during treatment are matched, andthe position/attitude conversion parameters for matching both the imagesof the data is obtained, whereby patient positioning at the time of thetreatment can be achieved.

PRIOR ART DOCUMENT Patent Document

-   [Patent Document 1] Japanese Laid-Open Patent Publication H09-277184    (page 2-3)

DISCLOSURE OF THE INVENTION Problems to be Solved by the Invention

Such an image matching method using the ICP method has characteristicsthat an image matching calculation result depends on a structure of thethree-dimensional point group data to be processed, and also depends onan initial state when the matching calculation starts. Especially, in acase in which slice thicknesses of the CT image data for the treatmentplan and the CT image data for the positioning operation are small andclose to their pixel spacing values, because of their higher datadensity, the error function becomes a shape having multiple localsolutions, and the solution does not converge to the correct answervalue (the optimal solution at which the error function takes theminimum value); as a result, the number of cases increases in which thesolution converges to a incorrect answer value (a local solutioncorresponding to a local minimal value of the error function).

Regarding an image matching operation in radiation treatment, the abovephenomenon is specifically explained using FIG. 12. Data arranged inlattice shapes with white circles at the vertices is assumed as pointgroup data of the CT image data for the treatment plan as reference(first point group data), while data with black squares at the verticesis assumed as point group data of the CT image data for the positioningoperation during the treatment as the matching target (second pointgroup data). Additionally, in this example, the lattice spacingdistances for both the point group data are assumed to be equal forsimplicity.

As represented in the figure, when the densities of the first pointgroup data and the second point group data are in higher states, in theN-th sequential and iterative calculation by the ICP method (FIG. 12(b)), in many cases nearest corresponding points are the same as those inthe (N−1)-th calculation (FIG. 12( a)) increase, and in some cases thenearest corresponding points do not vary also in the (N+1)-thcalculation; as a result, in such cases, the variation amount of theerror function remains at a slight level.

The above state is schematically represented in FIG. 13, in which arelative positional shift between the treatment plan data group and thepositioning operation data group (Δx) is taken on the horizontal axis,while a distribution of the error function (E) is on the longitudinalaxis. The error function reaches the minimum when the positional shiftbetween both the data groups does not occur (Δx=0), and has adistribution in which local minimums appear for every lattice-distanceshift (dl) between both the data groups (refer to a curve E1) from theminimum state. In a case of the state of both the point group datadensities being higher as described above, because the error function E1has a local minimal value also in the case of being shifted by onelattice unit from the optimal solution (Δx=0), a problem has occurred inthe ICP method that, due to the solution being determined to haveconverged at the local minimal value, probability of falling into alocal solution (incorrect answer value, Δx=Δx_(L)) is high.

An objective of the present invention, which is made to solve the abovedescribed problem, is to provide an image matching device having highermatching accuracy, by avoiding reaching a local solution so as to surelyconverge to the optimal solution in an automatic positioning calculationusing the ICP method, and to provide a patient positioning device usingthe image matching device.

Means for Solving the Problem

An image matching device and a patient positioning device using theimage matching device according to the present invention each include aCT image data reading unit for reading first CT image data and second CTimage data, a point group data generation unit for generating firstpoint group data and second point group data in a three-dimensionalspace from sliced images of the first CT image data and the second CTimage data, respectively, a point group data resolution conversion unitfor thinning-out at least one of the first point group data and thesecond point group data so as to extend a point-group-data array pitch,and an ICP calculation unit.

Here, the ICP calculation unit is characterized in that a conversionamount for the second point group data is obtained, using an ICP method,so that an error function of the first point group data and the secondpoint group data outputted from the point group data resolutionconversion unit is minimized.

Advantageous Effect of the Invention

According to the image matching device and the patient positioningdevice using this image matching device of the present invention,because the point group data resolution conversion unit thins out atleast one of the first point group data and the second point group dataso as to extend the array pitch of the point group data, and the ICPcalculation unit obtains, using the ICP method, a conversion amount forthe second point group data so that the error function of the firstpoint group data and the second point group data outputted from thepoint group data resolution conversion unit is minimized, as representedin FIG. 13 by the curved line (E2) after the point group data resolutionhas been converted, the distribution of the error function can bemodified so that the pitch where the local minimal values appear isextended by the extension of the lattice pitch.

Therefore, even when the solution of the error function before the pointgroup data resolution conversion (E1) is in a local minimal value(Δx=Δx_(L)), because, after the point group data resolution is converted(E2), that can be set not at the local minimal value, further solutionsearch is performed by the ICP calculation unit without erroneouslydetermining to have converged; as a result, it is possible to surelyconverge to the optimal solution (Δx=0).

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a view illustrating an overall system configuration withrespect to an image matching device and a patient positioning deviceaccording to Embodiment 1 of the present invention;

FIG. 2 is a diagram representing relationships among data processingunits configuring the image matching device and the patient positioningdevice according to Embodiment 1 of the present invention;

FIG. 3 is a view schematically representing the definition of acoordinate system in the image matching device according to Embodiment 1of the present invention;

FIG. 4 includes views explaining how to create point group data fromsliced images of CT image data in the image matching device according toEmbodiment 1 of the present invention;

FIG. 5 is a view illustrating an example in which point group data ofbone outline images is created in the image matching device according toEmbodiment 1 of the present invention;

FIG. 6 includes views representing states in which point group data isdivided into regions in the image matching device according toEmbodiment 1 of the present invention;

FIG. 7 is a schematic view illustrating the definition of a distancebetween an arbitrary point and a point group, in the image matchingdevice according to Embodiment 1 of the present invention;

FIG. 8 is a view illustrating relationships among data processing unitsconfiguring an image matching device and a patient positioning deviceaccording to Embodiment 2 of the present invention;

FIG. 9 is representation explaining how to calculate an evaluation scorewhen multi-layer multi templates are set using the image matching deviceaccording to Embodiment 2 of the present invention;

FIG. 10 is representation explaining how to calculate an evaluationscore when multi-layer multi templates are set using the image matchingdevice according to Embodiment 2 of the present invention;

FIG. 11 is representation explaining how to calculate an evaluationscore when multi-layer multi templates are set using the image matchingdevice according to Embodiment 2 of the present invention;

FIG. 12 includes views explaining a phenomenon in which a result ofimage-matching calculation using an ICP method falls into a localsolution; and

FIG. 13 is a graph explaining the difference between error-functiondistributions before and after point-group-data resolution conversionprocessing.

BEST MODE FOR CARRYING OUT THE INVENTION Embodiment 1

FIG. 1 is a view illustrating an overall system configuration withrespect to image matching, positioning, and radiation treatment using CTimage data, to which an image matching device and a patient positioningdevice according to Embodiment 1 of the present invention are assumed tobe applied. Numeral 1 denotes a CT simulator room, for creating atreatment plan before radiation treatment, in which a CT gantry 2, and atop board 3 of a bed for taking CT images are included, and, by laying apatient 4 on the top board 3, CT image data for a treatment plan istaken so that an affected part 5 is included.

On the other hand, numeral 6 denotes a treatment room for the radiationtreatment, in which a CT gantry 7, a rotatable treatment table 8, and atop board 9 provided over the rotatable treatment table 8 are included,and, by laying a patient 10 on the top board 9, CT image data for apositioning operation is taken so as to include an affected part 11 forthe treatment.

Here, the positioning operation means that positions of the patient 10and the affected part 11 during the treatment are determined from the CTimage data for the treatment plan, a body-position correction amount iscalculated so as to match the treatment plan, and then the position ofthe affected part 11 during the treatment is aligned so as to be at abeam irradiation center 12 during the radiation treatment. The positionalignment is realized by drive-controlling the rotatable treatment table8, with the patient 10 lying on the top board 9, to move the position ofthe top board 9. Regarding the rotatable treatment table 8,translation-and-rotation six-degree-of-freedom (6DOF) drive compensationcan be performed, and by rotating by 180 degrees the top board 9 of therotatable treatment table 8, the top board can also be moved from a CTimage taking position (indicated by solid lines in FIG. 1) to atreatment position where an irradiation head 13 is arranged (indicatedby broken lines in FIG. 1). Here, in FIG. 1, an example is representedin which the CT image taking position and the treatment position are in180 degree opposed-position relationship; however, the alignment is notlimited that, but their positional relationship may be at other anglessuch as 90 degrees.

Here, the CT image data for the treatment plan and the CT image data forthe positioning operation are transmitted to a positioning computer 14.Both the image matching device and the patient positioning deviceaccording to the present invention relate to computer software includedin this positioning computer, in which the image matching devicecalculates the body-position correction amounts (translation amounts androtation amounts), while the patient positioning device includes theimage matching device and also has a function for calculating parametersfor controlling, based on the body-position correction amounts, thedriving axes of the treatment table.

In the conventional positioning operation in the radiation treatment, apositional shift amount has been calculated by matching a DRR (digitallyreconstructed radiography) image generated from the CT image data forthe treatment plan and an X-ray perspective image taken in the treatmentroom during the treatment. Because, in the X-ray perspective image, animage of an affected part as a soft tissue cannot be clearly taken,position alignment using a bone is a fundamental method. The CTpositioning operation described in this embodiment has characteristicsthat, because the CT gantry 7 is placed together in the treatment room 6and the position alignment is performed using the CT image data justbefore the treatment and the CT image data for the treatment plan, theaffected part can be directly visualized, and the position alignment canbe performed at the affected part.

Next, calculation procedures of the body-position correction amounts inthe image matching device and the patient positioning device accordingto this embodiment will be explained. FIG. 2 is a configuration diagramillustrating relationships among data processing units constituting theimage matching device and the patient positioning device, in which animage matching device 15 a is configured with a CT image data readingunit 17, a point group data generation unit 18, a point group dataresolution conversion unit 19, a point group data region division unit20, a point group data noise removal unit 21, an ICP calculation unit22, and an MPR processing unit 23, and a device in which a treatmenttable control parameter calculation unit 24 is added to the imagematching device is a patient positioning device 16 a.

In the CT image data reading unit 17, CT image data for the treatmentplan (first CT image data) and that for the positioning operation(second CT image data) are read. The CT image data is constituted ofsliced images in DICOM (digital imaging and communication in medicine)format; however, the format is not especially limited thereto.

In the point group data generation unit 18, point group data based onthe CT image data for the treatment plan (first point group data) andthat based on the CT image data for the positioning operation (secondpoint group data) are generated from the sliced images of the CT imagedata for the treatment plan and the CT image data for the positioningoperation. The point group data here indicates point data aggregation ina three-dimensional space, in which, regarding the three-dimensionalspace, for example, as illustrated in FIG. 3, among sliced images 27, acoordinate system 29 is set on a sliced image including a CT origin 28corresponding to a reference point when the CT images are taken, and aright-handed coordinate system may be configured in such a way that theX-axis is in a horizontal direction in the sliced image plane, theY-axis is in a direction perpendicular to the X-axis in the sliced imageplane, and the Z-axis is in a direction perpendicular to the slicedimage plane. The scale of the coordinate system can be determined by apixel spacing value or a slice pitch that are tag information of theDICOM format file.

Specific point-group-data generation processing is described. Bybinarizing the sliced images by threshold processing, a bone region isextracted. Sequentially, edge extraction processing is performed on thebinarized image, thinning processing is performed, and thus a boneoutline image is obtained. In the edge extraction processing and thethinning processing, the Canny method or the like can be used; however,the method is not limited thereto, but other image matching techniquesthat can give a similar function may be applied. Point group data isgenerated from data on the outline in the bone outline image. The pointgroup data is a set of points having three-dimensional coordinates (x,y, z). FIG. 4 is schematic representation of an example in which, for asliced image among sliced images (FIG. 4( a)), a bone outline image iscreated (FIG. 4( b)), and point group data (FIG. 4( c)) is generatedfrom data on the outline in the bone outline image. FIG. 5 is schematicrepresentation of an example in which point group data is created fromthree bone outline images.

In the point group data resolution conversion unit 19, by thinning-outat least one of the first point group data for the treatment plan andthe second point group data for the positioning operation so as toextend the array pitch of the point group data, the data amount isadjusted. Providing that the resolution when the point group data isgenerated by the point group data generation unit 18 is defined to be1/1, the resolution in the case of thinning out one out of every twopoints therefrom can be defined to be ½, three out of every four pointsto be ¼, and seven out of every eight points to be ⅛. Also, in a case ofthe resolution being smaller than ⅛, it is assumed to be similarlydefined. As for the thinning-out order, the thinning-out operation isperformed in the identical sliced image plane by the appearance order ofthe points when raster scanning is performed, when processing overslices, the thinning direction may be determined to be, for example,from the head top to the head tail. However, the order has no meaning;therefore, processing of random thinning-out from the entire point groupdata may be performed. Moreover, the denominator with respect to thethinning-out operation is not needed to be a power of two.

In the point group data region division unit 20, each of the first pointgroup data and the second point group data generated in the point groupdata generation unit 18 and the point group data resolution conversionunit 19 is divided into regions. For example, a system may be introducedin which, by setting the barycentric position of the point group data asthe origin, dual division can be performed for each of the x-axis,y-axis, and z-axis. For example, if the division is performed only forthe x-axis, it means the data is divided into two sets, and if the dualdivision is performed for all the axes (x-axis, y-axis, and z-axis), itmeans the data is divided into eight sets in all. In this case, it isnot necessary to actually divide data to store as different variables orfiles, but a system may be introduced in which a region to which eachpoint of the point group data belongs is kept as an attribute valuethereof.

An example is illustrated in FIG. 6, in which the second point groupdata for the positioning operation and the first point group data forthe treatment plan are divided into four regions from R1 to R4 (FIG. 6(a)) and four regions from R1′ to R4′ (FIG. 6( b)), respectively. Boththe first point group data and the second point group data have to besimilarly divided into regions, where the divided regions of the firstpoint group data includes the respective divided regions of the secondpoint group data, and, considering the positional shift between both thepoint group data, by setting margin regions, the divided regions of thefirst point group data have to be larger than those of the second pointgroup data.

In contrast, in a case of the first point group data for the treatmentplan being matched with the second point group data for the positioningoperation, the divided regions of the second point group data are neededto include those of the first point group data by providing marginregions in the divided regions of the second point group data to makethe divided regions of the second point group data larger than those ofthe first point group data.

In the point group data noise removal unit 21, if the top board 3 or 9,or a part of clothes worn by the patient 4 or 10, etc., other than boneregions is imaged in the first point group data and the second pointgroup data generated in the point group data generation unit 18 and thepoint group data resolution conversion unit 19, its corresponding partof the data is removed. Because physical positions of the top boards 3and 9 are previously known, and regarding the top boards 3 and 9 and theclothes worn by the patient 4 and 10, their CT values range aroundvalues different from that of the bone, they can be determined to bedata other than those of target regions for image-matching; therefore,by utilizing the coordinate information and the CT values, these can beeasily removed.

In the ICP calculation unit 22, using the first point group data for thetreatment plan and the second point group data for the positioningoperation, generated through the point group data generation unit 18,the point group data resolution conversion unit 19, the point group dataregion division unit 20, and the point group data noise removal unit 21,the conversion amount (body-position correction amount) for the secondpoint group data is obtained, by calculating what conversion isperformed on positions and attitudes of the second point group data, tobe able to approximate the first point group data most, that is, so thatan error function between them is minimized.

Specific processing steps are represented as follows. As an indexrepresenting correspondence between two points in the three-dimensionalspace, the distance between the two points is used. Here, the distancebetween the two points x₁ and x₂ in the three-dimensional space isexpressed as d(x₁, x₂).

Moreover, assuming that NR point data (x(i): i=1, 2, . . . , NR) isincluded in a point group G, the distance between an arbitrary point pand the point group G is defined, as expressed by Equation 1, to be thedistance between a point nearest to the point p among points included inthe point group G, and the point p (refer to FIG. 7).

$\begin{matrix}{{d( {p,Z} )} = {\min\limits_{i}( {d( {p,{x(i)}} )} )}} & \lbrack {{Equation}\mspace{14mu} 1} \rbrack\end{matrix}$

Here, it is assumed that a point group composed of N_(ref) point data(x_(ref)(i): i=1, 2, . . . , N_(ref)) included in the first point groupdata (for the treatment plan) is G_(ref), while that composed ofN_(test) point data (x_(test)(j): j=1, 2, . . . , N_(test)) included inthe second point group data (for the positioning operation) is G_(test).

First, as Step 1, the distance between the point x_(ref)(i) included inthe first point group data G_(ref) and the second point group dataG_(test) is obtained, and by setting the point x_(test)(j) at that timeto be a corresponding point x′_(test)(i) to the x_(ref)(i),corresponding relationships between the first point group data G_(ref)and the second point group data G_(test) are obtained.

$\begin{matrix}{{d( {{x_{ref}(i)},G_{test}} )} = { {\min\limits_{1 \leq j \leq N_{test}}( {d( {{x_{ref}(i)},{x_{test}(j)}} )} )}\Rightarrow{x_{ref}(i)} ->{{x_{test}(j)}( {x_{test}^{\prime}(i)} )}}} & \lbrack {{Equation}\mspace{11mu} 2} \rbrack\end{matrix}$

Next, as Step 2, a conversion amount (rotation amount R, translationamount t) from the second point group data G_(test) to the first pointgroup data G_(ref) is obtained. Here, the rotation amount R is expressedin a 3×3 matrix, while the translation amount t is expressed in a 3×1matrix. Vector difference values each between a point obtained byconverting a point x′_(test)(i) in the second point group data G_(test)by the rotation amount R and the translation amount t, and the pointx_(ref)(i), which belong to the first point group data G_(ref),corresponding to the point x′_(test)(i) are obtained, and their squaresum is defined as an error e²(R, t) (Equation 3). A rotation amount R*and a translation amount t*, when the value of e²(R, t) is the minimum,is defined to be a conversion amount at that time (Equation 4).

$\begin{matrix}{{e^{2}( {R,t} )} = {\sum\limits_{i}{{{x_{ref}(i)} - {{Rx}_{test}^{\prime}(i)} - t}}^{2}}} & \lbrack {{Equation}\mspace{14mu} 3} \rbrack \\{( {R^{*},t^{*}} ) = {\arg \mspace{14mu} {\min\limits_{R,t}{e^{2}( {R,t} )}}}} & \lbrack {{Equation}\mspace{14mu} 4} \rbrack\end{matrix}$

Next, as Step 3, according to the conversion amount obtained by Step 1and Step 2, each of the points that belong to the second point groupdata G_(test) is converted; where k is defined as aniterative-calculation index in the ICP method.

x _(test) _(—) _(k+1)(i)=R* _(k) x _(test) _(—) _(k)(i)+t*_(k)  [Equation 5]

Iterative calculation is performed in Step 1 through Step 3. When avariation amount of an error in Step 2 by the iterative calculationreaches a predetermined threshold value or lower, the calculation iscompleted. Thus, values (rotation amount Rs*, translation amount is*)obtained by synthesizing all the conversion amounts (rotation amount R*,translation amount t*) obtained through the iterative calculation is tobe a conversion amount from the second point group data G_(test) to thefirst point group data G_(ref).

As described above, a corresponding operation is performed from each ofthe points included in the second point group data (for the positioningoperation) to the nearest point in the first point group data (for thetreatment plan), and thus the conversion amount (rotation amount R*,translation amount t*) that minimize an error function e²(R, t) definedby the sum of the distances between the positioning data group and thetreatment plan data group can be obtained by sequential and iterativecalculation.

Next, the MPR processing unit 23 is explained. The MPR (multi planarreconstruction/reformat) processing is a method of creating one volumedata set by stacking a number of sliced images of the CT image data, togenerate arbitrary sectional images on the basis of the data, which iscalled as “oblique processing”, or also as “double oblique processing”.Therefore, by using this processing, even if the data is image datawhich is not actually taken, such as a sagittal sectional image or acoronal sectional image, such data can be created byinterpolation-processing image data of an axial section which isactually taken, for reconstruction.

As an interpolation algorithm, non-linear interpolation algorithms suchas tricubic interpolation one, which is extended trilinear interpolation(TLI) one, can be also used other than linear interpolation algorithmssuch as the TLI. By setting a point (X, Y, Z) included in an initialsection such as an axial section, a sagittal section, and a coronalsection, and by setting a rotation amount (A, B, C) around each of thecoordinate axes when orthogonal coordinate axes are defined using thepoint as a rotation center and the initial section as a reference, anoblique section is determined, and image data in the oblique section canbe calculated.

If the CT image data for the treatment plan (the first CT image data)and that for the positioning operation (the second CT image data) areevaluated in respective MPR sections in which oblique-section creationparameters (X, Y, Z, A, B, C) are the same, the images do not match witheach other before the automatic positioning operation, that is, they areshifted from each other.

Then, in the results from the ICP calculation unit 22 (rotation amountRs*, translation amount ts*), by defining each component of thetranslation amount ts* as (ΔX, ΔY, ΔZ), and replacing the rotationamount Rs* with that (ΔA, ΔB, ΔC) around each axis of the X-axis,Y-axis, and Z-axis, the MPR section is created using oblique-sectioncreation parameters (X-ΔX, Y-ΔY, Z-ΔZ, A-ΔA, B-ΔB, C-ΔC) from the CTimage data for the positioning operation; thereby, an image in which theCT image data for the positioning operation is converted to approximatethe treatment plan CT image data can be created. Additionally, by usingthe MPR section having been initially created, and by using dataobtained by rotating and translating the CT image data for thepositioning operation (the second CT image data) by (ΔX, ΔY, ΔZ, ΔA, ΔB,ΔC), an image close to the CT image data for the treatment plan (thefirst CT image data) can also be obtained, and the matching can beperformed.

According to this operation, confirmation can be achieved whether bonestructures of the CT image data for the positioning operation and thatfor the treatment plan match with each other, and determination can beperformed whether the positioning result is appropriate or not.

As initial sections for checking the image, orthogonal three sectionsconstituted of the axial section, the sagittal section, and the coronalsection, etc., may be adopted, sections tilting from the axial section,the sagittal section, and the coronal section may be adopted, orcombinations of those may also be adopted. Moreover, a system may beconfigured in which a treatment-plan image and a positioning-operationimage are differentially displayed or overlappingly displayed, and thedifference therebetween is checked.

Inversely, by inverting the plus/minus sign of the result obtained bythe ICP calculation unit 22, the MPR section is created usingoblique-section creation parameters (X+ΔX, Y+ΔY, Z+ΔZ, A+ΔA, B+ΔB, C+ΔC)from the CT image data for the treatment plan; thereby, an image inwhich the CT image data for the treatment plan (the first CT image data)is converted to approximate that for the positioning operation (thesecond CT image data) can also be created.

In the treatment table control parameter calculation unit 24, the outputvalues from the ICP calculation unit 22 (total 6DOF consisting oftranslation three-axes [ΔX, ΔY, ΔZ] and rotation three-axes [ΔA, ΔB,ΔC]) are converted into parameters for controlling respective axes ofthe treatment table, to control the treatment table. According to thisoperation, the body-position correction amount is calculated so as tomatch the treatment plan, and thus the alignment can be performed sothat the affected part 11 during the treatment is placed at the beamirradiation center 12 of the radiation treatment.

As described above, according to the image matching device 15 a and thepatient positioning device 16 a of this embodiment, the point group dataresolution conversion unit 19 thins out at least one of the first pointgroup data and the second point group data so as to extend the arraypitch of the point group data, and the ICP calculation unit 22 obtains aconversion amount for the second point group data using the ICP methodso that the error function between the first and the second point groupdata outputted from the point group data resolution conversion unit 19is minimized; therefore, as represented in FIG. 13 by a curved line (E2)after the point group data resolution has been converted, distributionof the error function can be modified so that a pitch where the localminimal values appear is extended by the extension of the lattice pitch.

Therefore, because even if an error function (E1) before the point groupdata resolution conversion is at a local minimal value (Δx=Δx_(L)), theerror function (E2) after the conversion can be set not to take a localminimal value, further solution search is performed by the ICPcalculation unit 22 without erroneously determining that the solutionhas converged, and consequently it becomes possible to surely convergeto the optimal solution (Δx=0).

The image matching device 15 a and the patient positioning device 16 aaccording to this embodiment includes the region division unit 20 fordividing the first point group data into a plurality of regions, and fordividing the second point group data into a plurality of regionscorresponding thereto, where the ICP calculation unit 22 calculates onlybetween each region of the first point group data and that of the secondpoint group data corresponding thereto; therefore, a calculation amountwhen calculating the nearest corresponding points can be reducedcompared with a case of calculating all combinations of correspondingpoints for the entire region before dividing, so that high-speedprocessing can be realized. As for the calculation amount, regarding thefirst point group data for the treatment plan and the second point groupdata for the positioning operation, if each data region is divided intoN regions, the total calculation amount can be reduced approximately to1/N (exactly, because of the presence of margin regions, a valueslightly larger than 1/N is actually obtained).

The image matching device 15 a and the patient positioning device 16 aaccording to this embodiment further includes the point group data noiseremoval unit 21 for removing, from at least one of the first point groupdata and the second point group data, noise data in the part other thanthose of target regions for image-matching, and the ICP calculation unit22 calculates using the first point group data and the second pointgroup data from which the noise data is removed; therefore, erroneouscorrespondences between the first point group data for the treatmentplan and the second point group data for the positioning operation arereduced, and an effect is resultantly obtained that the most suitableresolution is introduced without falling into a local solution by theiterative calculation in the ICP calculation unit 22.

Here, in the above description, an example has been explained in which,in the point group data generation unit 18, a bone region is extracted,the edge extraction processing is performed on the binarized image, thethinning processing is performed to obtain a bone outline image, andusing this image the image matching is performed; however, instead ofthe bone region, the image matching can also be performed on an affectedpart or an organ to which the radiation treatment is to be performed. Inthis case, the extraction may be performed by binarizing the affectedpart or the organ using a specified CT value. Because the subsequentdata processing is similar to that for the bone region, the detaileddescription is omitted.

By performing the image matching for the affected part or the organ towhich the radiation treatment is performed, and, based on the outputvalues obtained by the ICP calculation unit 22 (total 6DOF consisting ofthe translation three-axes [ΔX, ΔY, ΔZ] and the rotation three-axes [ΔA,ΔB, ΔC]), and by converting the values into the parameters forcontrolling the axes of the treatment table by the treatment tablecontrol parameter calculation unit 24, to control the treatment table,an effect similar to the above can be obtained, and additionally, anadvantage is obtained that a template matching operation explained inthe following embodiment 2 can be made needless.

Embodiment 2

In an image matching device 15 b and a patient positioning device 16 baccording to this embodiment, a template setting unit 25 and a templatematching unit 26 are added to those represented in Embodiment 1, whoseconfiguration is represented in FIG. 8. According to this configuration,after the matching with a bone (global matching), matching using atemplate corresponding to an affected part (target cancer) (localmatching) can be performed, so that a matching operation and apositioning operation can be performed more precisely than those only bythe global matching. Because, regarding a soft tissue other than thehead and neck area, especially such as the lungs, liver, and prostate,bones are not present in the proximity thereof, a case might occur thataccuracy required for the radiation treatment cannot be satisfied onlyby the positioning operation with the bone; therefore, in this case, amethod is effective in which a template matching operation is performedby setting the neighborhood of the affected part (target cancer) as atemplate.

In the template setting unit 25, a template image is registered. Usingthe calculation result by the ICP calculation unit 22 (ΔX, ΔY, ΔZ, ΔA,ΔB, ΔC), the MPR section group created from the CT image data for thepositioning operation (second CT images) using the oblique-sectioncreation parameters (X-ΔX, Y-ΔY, Z-ΔZ, A-ΔA, B-ΔB, C-ΔC) is assumed tobe used. In this step, a characteristic region in which variation ofbrightness gradation in the MPR section-group converted from the CTimages for the positioning operation is relatively large may be selectedas a template.

In the template matching unit 26, a template matching operation isexecuted within a search range in sliced images of the CT image data forthe treatment plan (first CT images), a place is obtained where theircorrelation value reaches highest, and then a conversion-amountcompensation value of the second point group data is obtained so as totranslate the characteristic region to this place, as a furthercompensation value for the conversion amount of the second point groupdata obtained by the ICP calculation unit 22.

In this template matching operation according to this embodiment, anormalized cross correlation method as a robust method against uniformbrightness variations is assumed to be used as a method for obtainingthe correlation value. However, it is not limited to this method, but aprobability-based similarity such as a mutual information amount canalso be utilized as a correlation value. Moreover, a robust andhigh-speed method such as a partial template method (refer to *1) or ahierarchical search method (refer to *2) can also be used for thetemplate matching operation.

*1) Haruhisa Okuda, Manabu Hashimoto, Kazuhiko Simi and Kazunori Sasaki;“Robust Picture Matching Using Optimum Selection of Partial Templates”,IEEJ Transactions on Electronics, Information and Systems; Vol. 124, No.3, p. 629-636 (2004).

*2) Miwako Hirooka, Kazuhiko Sumi, Manabu Hashimoto and Haruhisa Okuda,“Hierarchical distributed template matching”, SPIE Machine VisionApplications in Industrial Inspection V, pp. 176-183 (1997).

In order to perform a matching operation more accurately than theone-piece template matching operation, by setting a plurality oftemplates for a plurality of CT images for the positioning operation(MPR-section group), a multi-layer multi-template matching method forpositioning by the plurality of template images can also be applied.Specifically, three characteristic regions included in respective threeconsecutive sliced images in the MPR section group are registered astemplate images. The numeral three is an example, and any number may beused as long as it is more than one. It would be convenient that, when atemplate is set to the central sliced image among the three consecutiveimages, the other templates are automatically set to the previous andthe next sliced images.

Each of the template images for the positioning operation is matchedwith all the sliced images of the CT image data for the treatment plan(first CT images), and the total sum of the matching scores (correlationvalues) with respect to the three templates is defined as a newevaluation score. The calculation of the new evaluation score isperformed while constraining the positional relationships of theprevious and next slices when the multi-layer multi templates are set.

The slice pitch of the CT images for the positioning operation when themulti-layer multi templates are set is assumed to be N, while that forthe treatment plan M. A case is represented in FIG. 9 in which the slicepitches of the CT images for the treatment plan and those for thepositioning operation are equal (N=M). A value obtained by summing aresult of a positioning-template previous score for the previous slice(P1), a result of a positioning-template central score (C2), and aresult of a positioning-template next score for the next slice (N3),P1+C2+N3, for example, is to be a new evaluation score at the specifiedslice.

Similarly, when the slice pitch condition is N=3M, a value obtained bysumming scores for every three slices, such as P1+C4+N7, is to be a newevaluation score (refer to FIG. 10). Moreover, when the slice pitchcondition is N=5M, a value obtained by summing scores for every fiveslices, such as P1+C6+N11, is to be a new evaluation score (refer toFIG. 11). In the examples in which the multi-layer multi templates areused as above represented in FIG. 9 through FIG. 11, N is assumed to belarger than M and to be its multiple number.

In the template matching unit 26, sliced images of the CT image data(first CT image data) and a plurality of template images are matched, aplace where the new evaluation score (sum of correlation values forevery template image) is highest is obtained, and a conversion amountcompensation value is obtained for the second point group data so as totranslate a plurality of characteristic regions to this place.

In this case, by removing, using the LMedS (least median squares)estimation method being a robust estimation method, results whosepositional errors are relatively large among N template-matchingresults, and thereby local variation influences can also be removed.According to this operation, template information adapted withoutconflicting the parameter estimation can be effectively used. Setting ofN templates in the multi-layer multi template matching operation is notlimited to the axial section, but the templates can be set, using theMPR processing, to the sagittal section or the coronal section, or maybe set by combining the orthogonal three sections. According to thisconfiguration, more stable results can be expected to be obtainedcomparing to a case of matching in a single direction.

In the treatment table control parameter calculation unit 24, after theMPR section according to the result obtained by the ICP calculation unit22 and the section after detailed translation compensation by thetemplate matching operation have been confirmed, output values obtainedby detailed-translation compensating, using the conversion amountcompensation values obtained by the template matching unit 26, theresults of the output values from the ICP calculation unit 22 (total6DOF consisting of the translation three-axes and the rotationthree-axes), are converted into parameters for controlling the axes ofthe treatment table to be transmitted, whereby the positioning operationcan be performed so that the affected part 11 during the treatment is tobe placed at the beam irradiation center 12.

Because the image matching device 15 b and the patient positioningdevice 16 b according to this embodiment include the point group dataresolution conversion unit 19, the point group data region division unit20, and the point group data noise removal unit 21 as described above,operations and effects similar to those represented in Embodiment 1 areobtained.

Moreover, the MPR processing unit 23 for creating from the second CTimage data, using the oblique-section creation parameters based on theconversion amount obtained by the ICP calculation unit 22, the MPRsection group which can be compared with the first CT image data, thetemplate setting unit 25 for registering as a template image thecharacteristic region selected from the MPR section group, and thetemplate matching unit 26 for obtaining, by matching the sliced imagesof the first CT image data and the template image, the place where thecorrelation value is highest, and for obtaining the conversion amountcompensation value for the second point group data so as to translatethe characteristic region to this place are included; therefore, aneffect is obtained that, by compensating (positioning) in detail at theposition of the affected part after matching with the bone structure,accurate matching can be performed.

Moreover, the template setting unit 25 registers as a plurality oftemplate images a plurality of characteristic regions existing inrespective consecutive MPR sections selected from the MPR section group,and the template matching unit 26 obtains, by matching the sliced imagesof the first CT image data and the plurality of template images, a placewhere the sum of the correlation values for the respective templateimages is highest, and obtains a conversion amount compensation valuefor the second point group data so as to translate the plurality ofcharacteristic regions to this place; therefore, template matchingequivalent to a case where three-dimensional volume data is used as thetemplate can be realized by combining a plurality of two-dimensionaltemplate matching. Accordingly, an effect is obtained that equivalentaccuracy can be realized without spending more time than thethree-dimensional template matching.

Moreover, an effect is obtained that, regarding the MPR section groupobtained by the MPR processing unit 23, if the matching is performed bycombining templates obtained from arbitrary sections at various angles,not only higher-speed processing can be performed than that of thethree-dimensional volume matching, but the accuracy thereof can bemaintained in the same level as that in the three-dimensional case.

EXPLANATION OF REFERENCES

-   15 a, 15 b: Image matching device-   16 a, 16 b: Patient positioning device-   17: CT image data reading unit-   18: Point group data generation unit-   19: Point group data resolution conversion unit-   20: Point group data region division unit-   21: Point group data noise removal unit-   22: ICP calculation unit-   23: MPR processing unit-   24: Treatment table control parameter calculation unit-   25: Template setting unit-   26: Template matching unit

1. An image matching device comprising: a CT image data reading unit forreading first CT image data and second CT image data; a point group datageneration unit for generating first point group data and second pointgroup data in a three-dimensional space from sliced images of the firstCT image data and the second CT image data, respectively; a point groupdata resolution conversion unit for thinning-out at least one of thefirst point group data and the second point group data so as to extend apoint-group-data array pitch; and an ICP calculation unit for obtaining,using an ICP method, a conversion amount for the second point group dataso that an error function of the first point group data and the secondpoint group data outputted from the point group data resolutionconversion unit is minimized.
 2. An image matching device as recited inclaim 1 further comprising a region division unit for dividing the firstpoint group data into a plurality of regions, and the second point groupdata into a plurality of regions corresponding thereto, wherein the ICPcalculation unit calculates only between each of the regions of thefirst point group data and each of the corresponding regions of thesecond point group data.
 3. An image matching device as recited in claim1 further comprising a point group data noise removal unit for removing,from at least one of the first point group data and the second pointgroup data, noise data in a portion other than a region where imagematching is performed, wherein the ICP calculation unit calculates usingthe first point group data and the second point group data where thenoise data has been removed.
 4. An image matching device as recited inclaim 1 further comprising: an MPR processing unit for creating from thesecond CT image data, using oblique-section creation parameters based onthe conversion amount obtained by the ICP calculation unit, an MPRsection group which can be compared with the first CT image data; atemplate setting unit for registering as a template image acharacteristic region selected from the MPR section group; and atemplate matching unit for obtaining, by matching the sliced images ofthe first CT image data and the template image, a place where acorrelation value is highest, and then obtaining a conversion-amountcompensation value for the second point group data so as to translatethe characteristic region to this place.
 5. An image matching device asrecited in claim 4 wherein the template setting unit registers as aplurality of template images a plurality of characteristic regionsexisting in respective consecutive MPR sections selected from the MPRsection group, and the template matching unit obtains, by matching thesliced images of the first CT image data and the plurality of templateimages, a place where the sum of correlation values for the respectivetemplate images is highest, and a conversion amount compensation valuefor the second point group data so as to translate the plurality ofcharacteristic regions to this place. 6-7. (canceled)
 8. A patientpositioning device comprising: an image matching device including: a CTimage data reading unit for reading first CT image data and second CTimage data; a point group data generation unit for generating firstpoint group data and second point group data in a three-dimensionalspace from sliced images of the first CT image data and the second CTimage data, respectively; a point group data resolution conversion unitfor thinning-out at: Least one of the first point group data and thesecond point group data so as to extend a point-group-data array pitch;and an ICP calculation unit for obtaining, using an ICP method, aconversion amount for the second point group data so that an errorfunction of the first point group data and the second point group dataoutputted from the point group data resolution conversion unit isminimized; and a treatment table control parameter calculation unit forcalculating parameters for controlling driving axes of a treatment tableaccording to the conversion amount obtained by the ICP calculation unit.9. A patient positioning device as recited in claim 8, wherein the imagematching device further includes a region division unit for dividing thefirst point group data into a plurality of regions, and the second pointgroup data into a plurality of regions corresponding thereto, and theICP calculation unit calculates only between each of the regions of thefirst point group data and each of the corresponding regions of thesecond point group data.
 10. A patient positioning device as recited inclaim 8, wherein the image matching device further includes a pointgroup data noise removal unit for removing, from at least one of thefirst point group data and the second point group data, noise data in aportion other than a region where image matching is performed, and theICP calculation unit calculates using the first point group data and thesecond point group data where the noise data has been removed.
 11. Apatient positioning device as recited in claim 8, wherein the imagematching device further includes: an MPR processing unit for creatingfrom the second CT image data, using oblique-section creation parametersbased on the conversion amount obtained by the ICP calculation unit, anMPR section group which can be compared with the first CT image data; atemplate setting unit for registering as a template image acharacteristic region selected from the MPR section group; and atemplate matching unit for obtaining, by matching the sliced images ofthe first CT image data and the template image, a place where acorrelation value is highest, and then obtaining a conversion-amountcompensation value for the second point group data so as to translatethe characteristic region to this place; and the treatment table controlparameter calculation unit calculates the parameters for controlling thedriving axes of the treatment table according to the conversion amountobtained by the ICP calculation unit and the conversion amountcompensation value obtained by the template matching unit.
 12. A patientpositioning device as recited in claim 11, wherein the template settingunit registers as a plurality of template images a plurality ofcharacteristic regions existing in respective consecutive MPR sectionsselected from the MPR section group, and the template matching unitobtains, by matching the sliced images of the first CT image data andthe plurality of template images, a place where the sum of correlationvalues for the respective template images is highest, and a conversionamount compensation value for the second point group data so as totranslate the plurality of characteristic regions to this place.